The data revealed that of the data collected, coral bleaching began in 1973. Data collection and ended in 2011. 50% of the bleached coral data was collected between 1998 and 2004. The average year for coral with a bleached severity code greater than 0 is 2001. The average severity code of the data is 0.8992.
<<<<<<< HEAD =======library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0 ✔ purrr 0.3.5
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.3 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(dplyr)
library(tidyr)
library(readr)
library(knitr)
coral_data <- read_csv("https://raw.githubusercontent.com/info201b-au2022/project-group-28/main/data/CoralBleaching.csv")
## Rows: 6190 Columns: 28
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (19): REGION, SUBREGION, COUNTRY, LOCATION, DEPTH, BLEACHING_SEVERITY, C...
## dbl (9): ID, LAT, LON, MONTH, YEAR, SEVERITY_CODE, MORTALITY_CODE, RECOVERY...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
coral_table <- coral_data %>%
select(COUNTRY, CORAL_SPECIES, BLEACHING_SEVERITY, WATER_TEMPERATURE, OTHER_FACTORS) %>%
group_by(COUNTRY) %>%
filter(CORAL_SPECIES == "Acropora"| CORAL_SPECIES == "Montipora"|
CORAL_SPECIES == "Pocillopora"| CORAL_SPECIES == "Porites", na.rm = TRUE) %>%
head(coral_table, n = 54)
kable(coral_table)
>>>>>>> 88485599372a812fc915fdbc93f370c525be727b
| COUNTRY | CORAL_SPECIES | BLEACHING_SEVERITY | WATER_TEMPERATURE | OTHER_FACTORS |
|---|---|---|---|---|
| Tanzania | Acropora | HIGH | 3 to 5C above normal | NA |
| Comoros | Acropora | HIGH | NA | NA |
| Comoros | Acropora | HIGH | NA | NA |
| Madagascar | Acropora | Medium | NA | NA |
| Mauritius | Acropora | Medium | NA | Cyclone Anacelle produced wet & clooudy weather in Feb. |
| Réunion | Acropora | Low | NA | NA |
| Réunion | Acropora | HIGH | 1.5c above mean | NA |
| Réunion | Pocillopora | Medium | NA | NA |
| Mexico | Pocillopora | Medium | 30c | NA |
| Mexico | Pocillopora | Medium | 30c | NA |
| Costa Rica | Porites | HIGH | NA | NA |
| Costa Rica | Pocillopora | Medium | NA | NA |
| Japan | Acropora | HIGH | NA | NA |
| Japan | Acropora | HIGH | NA | NA |
| Chagos Archipelago (UK) | Acropora | HIGH | NA | NA |
| India | Acropora | HIGH | NA | NA |
| India | Acropora | HIGH | NA | NA |
| India | Montipora | HIGH | NA | NA |
| Sri Lanka | Acropora | HIGH | NA | NA |
| Sri Lanka | Acropora | HIGH | NA | NA |
| Malaysia | Porites | Low | NA | NA |
| Thailand | Acropora | Low | NA | NA |
| Thailand | Acropora | HIGH | NA | NA |
| Australia | Acropora | Low | NA | NA |
| Australia | Acropora | HIGH | 27-29C | NA |
| Australia | Acropora | HIGH | 27-29C | NA |
| Australia | Acropora | Severity Unknown | NA | NA |
| Australia | Acropora | Severity Unknown | NA | NA |
| Australia | Acropora | Low | NA | NA |
| Australia | Acropora | HIGH | NA | NA |
| Torres Strait & Great Barrier Reef | Acropora | HIGH | NA | NA |
| United Arab Emirates | Acropora | HIGH | 34c | NA |
| United Arab Emirates | Acropora | HIGH | 34c | NA |
| United Arab Emirates | Acropora | HIGH | 2C above average, maximum recorded 35C, anomality start in April and lasted till Sept. | NA |
| United Arab Emirates | Acropora | Low | 2C above average, maximum recorded 35C, anomality start in April and lasted till Sept. | coral disease reduced |
| American Samoa | Porites | Low | NA | NA |
| Fiji | Acropora | HIGH | 30-31c | NA |
| Fiji | Acropora | HIGH | NA | NA |
| Fiji | Acropora | Medium | peaked at 30-30.5oC | NA |
| Fiji | Acropora | HIGH | 31-32c | NA |
| Fiji | Acropora | Low | 30.5-31C | NA |
| Fiji | Acropora | Low | peaked at 30-30.5oC | NA |
| Fiji | Acropora | HIGH | peaked at 30-30.5oC | NA |
| Fiji | Acropora | HIGH | peaked at 30-30.5oC | NA |
| Fiji | Pocillopora | Medium | peaked at 30-30.5oC | NA |
This table shows the necessary elements and value we used to create our visualizations and answer our research questions. The table includes the information on four main species of corals. It includes the location, bleaching level, and factors to the corals’ bleaching.
<<<<<<< HEADThe chart I included displays what kind of coral species are most =======
#Chart 1
library(tidyverse)
library(dplyr)
library(tidyr)
library("ggplot2")
name = c("Acropora", "Montipora", "Pocillopora", "Porites")
Reef_Size = c("small (< 10 cm)", "medium (10-50 cm)", "large (> 50 cm)")
value = c(56, 12, 5, 3, 50, 22, 11, 1, 55, 12, 48, 0)
corals <- data.frame(name, Reef_Size, value, stringsAsFactors = FALSE)
ggplot(data = corals, aes(x = name, y = value, fill = Reef_Size)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Coral Species vs Bleaching Susceptibility",
x = "Coral Species",
y = "Bleaching Susceptibilty (percentage)")
The chart I included displays what kind of coral species are most
>>>>>>> 88485599372a812fc915fdbc93f370c525be727b
susceptible to coral bleaching. This bar graph shows four of the most
common coral species and their resistance to bleaching based on their
coral reef size. We can see that the most susceptible species corals are
Acropora and the most resistant are the Porites species.
#Chart 2
library(tidyverse)
library(sf)
## Linking to GEOS 3.10.2, GDAL 3.4.2, PROJ 8.2.1; sf_use_s2() is TRUE
library(mapview)
library(readr)
coral_data <- read_csv("https://raw.githubusercontent.com/info201b-au2022/project-group-28/main/data/CoralBleaching.csv")
## Rows: 6190 Columns: 28
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (19): REGION, SUBREGION, COUNTRY, LOCATION, DEPTH, BLEACHING_SEVERITY, C...
## dbl (9): ID, LAT, LON, MONTH, YEAR, SEVERITY_CODE, MORTALITY_CODE, RECOVERY...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
filtered_coral_data <- filter(coral_data, SEVERITY_CODE > 0)
mapview(filtered_coral_data, xcol = "LON", ycol = "LAT", crs = 4269,
grid = FALSE)
<<<<<<< HEAD
=======
>>>>>>> 037da52f444e0a0dddae5ecd803789334c274478
>>>>>>> 88485599372a812fc915fdbc93f370c525be727b
This map shows the longitude and latitude of all the locations where coral bleaching has a severity code greater than 0 in this data set. As the map shows, coral bleaching affects coral reefs all over the world. It is not just concentrated to one area. The data points appear to be more concentrated north of Australia and between North America and South America. This is also where a larger number of coral reefs are located. This is helpful to understand that coral bleaching does not necessarily have any correlation with a particular region of the world.
<<<<<<< HEADThis data visualization shows a general timeline of severe coral =======
#Chart 3
library(tidyverse)
library("ggplot2")
severe_coral_bleaching_events <- read.csv("https://raw.githubusercontent.com/info201b-au2022/project-group-28/main/data/coral-bleaching-events.csv")
ggplot(severe_coral_bleaching_events, aes(x=Year, y=Severe.bleaching.events...30..bleached., color=Entity)) +
geom_point() +
labs(title = "Severe Coral Bleaching Events Over Time",
x = "Year",
y = "Number of Severe Bleaching Events >30% Bleached")
This data visualization shows a general timeline of severe coral
>>>>>>> 88485599372a812fc915fdbc93f370c525be727b
bleaching events from the year 1930 to 2016, and is organized by general
location, which in this case is the ocean’s region. The x-axis shows the
change in time, while the y-axis shows the number of severe bleaching
events that occurred that year. The data points are organized by
location, with a different color representing each region. Overall,
Australasia has the most frequent severe coral bleaching events with the
exception of the World category.